A high-performance and endurable SSD cache for parity-based RAID

Chu, Li; Feng, Dan; Hua, Yu; Wang, Fang

doi:10.1007/s11704-017-6523-9

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…For example, no region generation is performed, and the regression can only be performed on 7 × 7 sub-regions to locate the target very accurately. SSD is based on a forward-propagating CNN network, which generates many fixed-size outer frames, and the possibility that each frame contains objects, and then performs a non-maximum suppression method to obtain the final predicted value [34]. After the VGG-16 network, the SSD adds convolutional feature maps with decreasing resolution layer by layer.…”

Section: ) Non-maximum Suppressionmentioning

confidence: 99%

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

Mei-mei

Shu

2020

IEEE Access

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At present, human body moving target detection and recognition algorithms based on deep learning have made breakthrough progress. However, in some applications with high real-time requirements, the existing deep learning real-time detection and recognition network is difficult to achieve high detection accuracy. Therefore, how to achieve accurate positioning and recognition of human moving targets while ensuring real-time detection is still an urgent problem in this field. Based on the single shot multi-box detector (SSD) real-time detection network, this paper proposes a real-time detection positioning and recognition network based on multi-scale feature fusion (IMFF-SSD), which improves the positioning accuracy and identification accuracy. First, this article analyzes the multi-scale features extracted from the SSD network. It combines the position-sensitive information provided by low-level detail features with the context information provided by high-level semantic features through feature fusion, which effectively improves positioning accuracy of the target prediction layer in the SSD network. Secondly, a feature embedded prediction structure is designed to strengthen the semantics of target features without changing the spatial resolution of the SSD prediction layer, and embed low-scale detailed features in high-semantic features for collaborative prediction of targets. This improves the accuracy of the SSD network's recognition of human moving targets at all scales. The experimental results show that by combining the above two improvements, the real-time monitoring and recognition network based on multi-scale feature fusion proposed in this paper has achieved a greater degree of improvement in positioning accuracy and motion recognition accuracy than the original SSD, which is better than some current the human body moving object detection and recognition algorithm has great advantages. INDEX TERMS Deep learning, real-time, detection and motion recognition, multi-scale feature fusion.

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Section: ) Non-maximum Suppressionmentioning

confidence: 99%

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

Mei-mei

Shu

2020

IEEE Access

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“…Disks are the essential equipment for data storage, and their reliability is directly related to the security of stored data. Although there are a series of defense mechanisms in storage, such as RAID, the data centers will still face the problem of data loss when disks are damaged [2]. Therefore, the data in the damaging disks will be backed up in time to improve the security of the data if the disk failures can be accurately predicted [3].…”

Section: Introductionmentioning

confidence: 99%

Incremental Prediction Model of Disk Failures Based on the Density Metric of Edge Samples

Gao

Zha

et al. 2019

IEEE Access

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Disks are the main equipment for data storage in data centers. The prediction of disk failure is of great significance for the reliability and security of data. On account of the few abnormal samples in the disk datasets, it is difficult to satisfy the requirement of supervised and semi-supervised algorithms for the number of abnormal data while the unsupervised algorithms have poor performance on recall rate when solving the problems of local anomalies and wrapped anomalies. This paper presents an incremental learning disk failure prediction model using the density metric of edge samples. An isolation region is built by searching the nearest neighbor of each sample. We calculate the nearest training point of the test point which is not a global anomaly and the nearest training point of the obtained nearest training point by Euclidean distance. The global metric of abnormal degree of the test sample comes from the ratio of the radius of the region where the two nearest training points are located. Then, the local metric of abnormal degree of the test sample comes from the ratio between the nearest distance from the test point to the edge of the training point region and the radius of the region. Abnormal scores of test points can be obtained by combining two measurements. We identify the SMART attributes that are significantly related to disk failures and promote their weights in the next time the attributes are inputted. The experiments are carried on the synthetic and public datasets which contain local anomalies and wrapped anomalies. The proposed method outperforms the typical unsupervised algorithms such as iNNE, iForest and LOF, and the achieved recall rates increase at most 7%. Furthermore, the contrast tests on the public disk datasets also verify the proposed method has better performance on recall rate.INDEX TERMS Disk failures prediction, nearest neighbor, edge density metric, incremental learning.

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A-Cache: Asymmetric Buffer Cache for RAID-10 Systems Under a Single-Disk Failure to Significantly Boost Availability

Zhou

Jiang

Cao

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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A high-performance and endurable SSD cache for parity-based RAID

Cited by 4 publications

References 19 publications

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

Incremental Prediction Model of Disk Failures Based on the Density Metric of Edge Samples

A-Cache: Asymmetric Buffer Cache for RAID-10 Systems Under a Single-Disk Failure to Significantly Boost Availability

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